Advertisement

A comparative study on effects of some exclusive conditions in fuzzy co-clustering for collaborative filtering

  • Katsuhiro Honda
  • Seiki Ubukata
  • Akira Notsu
Original Research

Abstract

Fuzzy co-clustering is a promising approach for efficiently realizing collaborative filtering, in which personalized recommendation is achieved by summarizing the intrinsic user-item preferences through dual clustering of users and items in cooccurrence information matrices. In cases of applying fuzzy co-clustering, we can select roughly three partition models supported by different partition constraints: user targeting partition, (weak) dual exclusive partition and their hybrid approach. This paper presents a comparative study on the applicability of the three partition models to collaborative filtering tasks through empirical demonstration with two real world data sets. The experimental results reveal that user targeting partition is most suitable for the task while dual exclusive partition can also be used with sparse data sets.

Keywords

Fuzzy co-clustering Collaborative filtering Exclusive condition 

References

  1. Aggarwal CC (2016) Recommender Systems. Springer, New YorkCrossRefGoogle Scholar
  2. Badaro G, Hajj H, El-Hajj W, Nachman L (2013) A hybrid approach with collaborative filtering for recommender systems. In: Proceedings of 9th international wireless communications and mobile computing conference, pp 349–354Google Scholar
  3. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New YorkCrossRefzbMATHGoogle Scholar
  4. Bezdek JC, Hathaway RJ, Huband JM (2007) Visual assessment of clustering tendency for rectangular dissimilarity matrices. IEEE Trans Fuzzy Syst 15:890–903CrossRefGoogle Scholar
  5. Cui B, Jin H, Liu Z, Deng J (2015) Improved collaborative filtering with intensity-based contraction. J Ambient Intell Hum Comput 6:661–674CrossRefGoogle Scholar
  6. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 39:1–38MathSciNetzbMATHGoogle Scholar
  7. Herlocker JL, Konstan JA, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of 22nd conference on research and development in information retrieval, pp 230–237Google Scholar
  8. Holmes I, Harris K, Quince C (2012) Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS One 7–2:e30126CrossRefGoogle Scholar
  9. Honda K (2016) Fuzzy co-clustering and application to collaborative filtering. In: Huynh VN, Inuiguchi M, Le B, Le B, Denoeux T (eds) Integrated uncertainty in knowledge modelling and decision making (IUKM 2016), Lecture Notes in Computer Science, vol 9978. Springer, New York, pp 16–23Google Scholar
  10. Honda K, Notsu A, Ichihashi H (2010) Collaborative filtering by sequential user-item co-cluster extraction from rectangular relational data. Int J Knowl Eng Soft Data Paradig 2:312–327CrossRefGoogle Scholar
  11. Honda K, Muranishi M, Notsu A, Ichihashi H (2013) FCM-type cluster validation in fuzzy co-clustering and collaborative filtering applicability. Int J Comput Sci Netw Secur 13:24–29Google Scholar
  12. Honda K, Oshio S, Notsu A (2015) Fuzzy co-clustering induced by multinomial mixture models. J Adv Comput Intell Inform 19:717–726CrossRefGoogle Scholar
  13. Honda K, Nakano T, Oh C-H, Ubukata S, Notsu A (2015) Partially exclusive item partition in MMMs-induced fuzzy co-clustering and its effects in collaborative filtering. J Adv Comput Intell Intell Inform 19:810–817CrossRefGoogle Scholar
  14. Konstan JA, Miller BN, Maltz D, Herlocker JL, Gardon LR, Riedl J (1997) Grouplens: applying collaborative filtering to usenet news. Commun ACM 40–3:77–87CrossRefGoogle Scholar
  15. Koren Y (2010) Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans Knowl Discov Data 4–1:1CrossRefGoogle Scholar
  16. Kummamuru K, Dhawale A, Krishnapuram R (2003) Fuzzy co-clustering of documents and keywords. Proc IEEE Int Conf Fuzzy Syst 2:772–777Google Scholar
  17. Lee D-D, Seung H-S (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401–6755:788–791zbMATHGoogle Scholar
  18. Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, pp 76–80Google Scholar
  19. MacQueen JB (1967) Some methods of classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematics, statistics and probability, pp 281-297Google Scholar
  20. Miyamoto S, Ichihashi H, Honda K (2008) Algorithms for fuzzy clustering. Springer, New YorkzbMATHGoogle Scholar
  21. MovieLens Web Page. http://www.movielens.org/
  22. Oh C-H, Honda K, Ichihashi H (2001) Fuzzy clustering for categorical multivariate data. In: Proceedings of joint 9th IFSA world congress and 20th NAFIPS international conference, pp 2154–2159Google Scholar
  23. Oja E, Ilin A, Luttinen J, Yang Z (2010) Linear expansions with nonlinear cost functions: modeling, representation, and partitioning. In: 2010 IEEE world congress on computational intelligence, plenary and invited lectures, pp 105–123Google Scholar
  24. Pla Karidi D, Stavrakas Y, Vassiliou Y (2017) Tweet and followee personalized recommendations based on knowledge graphs. J Ambient Intell Hum Comput.  https://doi.org/10.1007/s12652-017-0491-7
  25. Rigouste L, Cappé O, Yvon F (2007) Inference and evaluation of the multinomial mixture model for text clustering. Inf Process Manag 43(5):1260–1280CrossRefGoogle Scholar
  26. Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009–421425:1–19CrossRefGoogle Scholar
  27. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240–4857:1285–1289MathSciNetCrossRefzbMATHGoogle Scholar
  28. Tjhi W-C, Chen L (2006) A partitioning based algorithm to fuzzy co-cluster documents and words. Pattern Recognit Lett 27:151–159CrossRefGoogle Scholar
  29. Tjhi W-C, Chen L (2008) A heuristic-based fuzzy co-clustering algorithm for categorization of high-dimensional data. Fuzzy Sets Syst 159:371–389MathSciNetCrossRefzbMATHGoogle Scholar
  30. Tsuda K, Minoh M, Ikeda K (1996) Extracting straight lines by sequential fuzzy clustering. Pattern Recognit Lett 17:643–649CrossRefGoogle Scholar
  31. Xiao J, Wang M, Jiang B, Li J (2017) A personalized recommendation system with combinational algorithm for online learning. J Ambient Intell Hum Comput.  https://doi.org/10.1007/s12652-017-0466-8

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Graduate School of EngineeringOsaka Prefecture UniversitySakaiJapan
  2. 2.Graduate School of Humanities and Sustainable System SciencesOsaka Prefecture UniversitySakaiJapan

Personalised recommendations